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Daily Dose of Data Science | Avi Chawla | Substack

Python offers four distinct approaches to parallel processing: threads (lightweight but limited by GIL for CPU tasks), multiprocessing (true parallelism with separate processes and memory), coroutines (cooperative multitasking for I/O operations), and subinterpreters (isolated environments with less overhead than multiprocessing). Threads and coroutines work best for I/O-bound tasks, while multiprocessing and subinterpreters enable true CPU parallelism. Python 3.13's free-threaded builds allow disabling the GIL, making threads viable for CPU-bound work. Choose based on whether your task is CPU or I/O-bound, the overhead tolerance, and need for memory isolation.

4 Parallel Processing Techniques in Python

Training LLM Agents using RL without writing any custom reward functions

​ 4 parallel processing techniques in Python ​